{"id":"W2996538552","doi":"","title":"Blind Deconvolution of Seismograms Regularized via Minimum Support","year":2010,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Seismogram; Deconvolution; Convolution (computer science); Source function; Algorithm; Blind deconvolution; Blind signal separation; Gaussian; Mathematics; Computer science; Geology; Physics; Seismology; Channel (broadcasting); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00153172,0.000174072,0.0002335761,0.0001739301,0.00009381664,0.0001188041,0.0007568412,0.0002337654,0.000004231867],"category_scores_gemma":[0.0002870584,0.0001780313,0.0001055567,0.0002913702,0.00009181687,0.0004797449,0.0001488565,0.0004024371,0.00004327168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001543224,"about_ca_system_score_gemma":0.0001250961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001133731,"about_ca_topic_score_gemma":0.00105268,"domain_scores_codex":[0.9982808,0.00007418729,0.0005979486,0.0003973832,0.0003323053,0.0003173448],"domain_scores_gemma":[0.9982611,0.000187468,0.000466753,0.0007152515,0.000233888,0.0001355523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000109958,0.0009733943,0.0169406,0.0001287962,0.0001150223,0.00005675923,0.006053418,0.0006800359,0.886731,0.01703897,0.00390766,0.06726437],"study_design_scores_gemma":[0.003926472,0.00084491,0.1048296,0.0002182906,0.00007549916,0.0002751857,0.0001423879,0.1023331,0.7286572,0.02723729,0.02974595,0.001714085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9157308,0.00001015521,0.07266416,0.0007222458,0.0004135632,0.0002441282,0.000001381603,0.0004610258,0.009752577],"genre_scores_gemma":[0.8352066,0.00000291726,0.1642306,0.0002668957,0.00005794295,0.00001258957,0.00001042384,0.00001368492,0.0001982831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1580739,"threshold_uncertainty_score":0.7259907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608242731724071,"score_gpt":0.26346235726214,"score_spread":0.2473799299448993,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}